Meta-learning [1]} [2]} [3]}[4]} [5]} [6]} [7]}for few-shot learning tries to construct the relevances between samples in the base classes for generalizing the model to the new classes. These methods can learn the common structure relationship between samples by training on the collection of the separated tasks.In terms of the coupling between the model and the data, meta-learning can mainly be divided into two groups.
